A solid state drive (“SSD”) is a non-volatile storage device that stores persistent data in solid state based memory. Due to improvements in cost, power and speed, SSD's have become more popular for storing enterprise data or other large sets of data that are often referred to as “Big Data.”
Big Data analytics involves running analytical algorithms on large sized unstructured data (on the order of gigabytes, terabytes or higher) to derive information and patterns that help make decisions pertaining to business, security, human behavior, economics, social trends, etc. The data can be super-sized files including text data, video data, voice data, numbers, etc. A typical data analytics program flow involves an analytics application reading a set of files from a hard disk drive (“HDD”) or SSD into a host machine's (e.g., an analytics server) memory, running a data analytics algorithm on the ‘in memory’ data, and utilizing the results for decision making. This approach suffers from the bottleneck of reading huge data sets from storage (e.g., HDD or SSD) into host memory over a storage network (“SAN”). Additionally, the above-described approach for Big Data analytics can be expensive both in terms of host system memory requirements and network usage.